{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Design 100 qubits programmatically"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prerequisite\n",
    "A working local installation of Ansys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## N_x by N_y grid of qubits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A simple example of generating a large grid of qubits.\n",
    "\n",
    "Using TransmonPocket and RouteMeander, we can generate a large, varied grid array of qubits. This is not a practical design given the lack of readouts or control lines to the qubits, just an example for how to make a design algorithmically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import qiskit_metal as metal\n",
    "from qiskit_metal import designs, MetalGUI\n",
    "from qiskit_metal import Dict, Headings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "design = designs.DesignPlanar()\n",
    "gui = MetalGUI(design)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_metal.qlibrary.qubits.transmon_pocket import TransmonPocket\n",
    "from qiskit_metal.qlibrary.tlines.meandered import RouteMeander"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "design.overwrite_enabled = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Design"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can vary how big we want the grid to be by changing N_x/N_y (number of qubits along the x/y axis).\n",
    "Be careful as very large arrays can take a fair bit of time to generate. We modify the chip size so it contains all of the qubits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "N_x =4\n",
    "N_y =3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "design.chips.main.size.size_x = str((N_x+1) * 3)+'mm'\n",
    "design.chips.main.size.size_y = str((N_y+1) * 3)+'mm'\n",
    "\n",
    "design.chips.main.size.center_x = str((N_x-1) * 1.5)+'mm'\n",
    "design.chips.main.size.center_y = str((N_y-0.5) * 1.5)+'mm'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we generate the qubits. We use some simple math to generate the offset pattern in order to make the bus resonators easier to connect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loop to generate and draw the qubits\n",
    "for x in range(N_x):\n",
    "    for y in range(N_y):\n",
    "        options = dict(pos_x= str(x*3000)+'um', pos_y = str(y*3000 + (x%2)*1500)+'um', orientation = \"-90\",\n",
    "                      connection_pads = dict(\n",
    "                            B0 = dict(loc_W=-1, loc_H=-1, pad_width='75um'),\n",
    "                            B1  = dict(loc_W=-1, loc_H=+1, pad_width='120um'),\n",
    "                            B2  = dict(loc_W=+1, loc_H=-1, pad_width='120um'),\n",
    "                            B3 = dict(loc_w = +1, loc_H = +1, pad_width='90um')))\n",
    "\n",
    "        \n",
    "        obj=TransmonPocket(design,'Q_'+str(x)+'_'+str(y),options)\n",
    "        \n",
    "        \n",
    "gui.rebuild()\n",
    "gui.autoscale()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we generate the route meanders. As we used consistent naming schemes for the qubits and pins, we can loop through them with out issue.\n",
    "We also have the length vary based on which qubit is being connected, such that no qubit should be connected to two resonators of the same frequency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in range(N_x):\n",
    "    for y in range(N_y):\n",
    "        #\"upward\" connection, avoids drawing connectors for 'top' row. Changes connector length by +/-50um to avoid frequency collisions\n",
    "        if y<(N_y-1):\n",
    "            connectorAD = RouteMeander(design,'CU_'+str(x)+'_'+str(y),options = dict(total_length = str(7+(y%2)*0.5)+'mm',\n",
    "                                                                                    fillet = \"99um\",lead=dict(\n",
    "                                                                                    start_straight='0.5mm',\n",
    "                                                                                    end_straight='0.25mm'),\n",
    "                                                                                    meander = dict(asymmetry='-700um'),\n",
    "                                                                                    pin_inputs = dict(\n",
    "                                                                                        start_pin=dict(\n",
    "                                                                                            component ='Q_'+str(x)+'_'+str(y),\n",
    "                                                                                            pin = 'B0'), \n",
    "                                                                                        end_pin=dict(\n",
    "                                                                                            component='Q_'+str(x)+'_'+str(y+1),\n",
    "                                                                                            pin='B3'))))\n",
    "            \n",
    "        #\"sideways\" connection, avoids drawing for far right col, and for top qubit in odd col. Changes connector length by +/- 25um \n",
    "        #to avoid frequency collisions\n",
    "        if x<(N_x-1) and (not(x%2 and y==(N_y-1))):\n",
    "            connectorBC = RouteMeander(design,'CS_'+str(x)+'_'+str(y),options= dict(total_length = str(6+(y%2)*0.5)+'mm',\n",
    "                                                                                    fillet = \"99um\",lead=Dict(\n",
    "                                                                                        start_straight='0.3mm',\n",
    "                                                                                        end_straight='0.25mm'),\n",
    "                                                                                    meander = Dict(asymmetry='-200um'),\n",
    "                                                                                    pin_inputs = Dict(\n",
    "                                                                                        start_pin=Dict(\n",
    "                                                                                            component ='Q_'+str(x)+'_'+str(y),\n",
    "                                                                                            pin = 'B1'), \n",
    "                                                                                        end_pin=Dict(\n",
    "                                                                                            component='Q_'+str(x+1)+'_'+str(y+(x%2)),\n",
    "                                                                                             pin='B2'))))\n",
    "            \n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gui.screenshot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can quickly check if we have designed the qubits well with an LOM analysis. \n",
    "First we select the design that we want to analyse and the tool to use for any simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_metal.analyses.quantization import LOManalysis\n",
    "c1 = LOManalysis(design, \"q3d\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can review and update the Analysis default setup following the examples in the next two cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'Setup',\n",
       " 'reuse_selected_design': True,\n",
       " 'reuse_setup': True,\n",
       " 'freq_ghz': 5.0,\n",
       " 'save_fields': False,\n",
       " 'enabled': True,\n",
       " 'max_passes': 15,\n",
       " 'min_passes': 2,\n",
       " 'min_converged_passes': 2,\n",
       " 'percent_error': 0.5,\n",
       " 'percent_refinement': 30,\n",
       " 'auto_increase_solution_order': True,\n",
       " 'solution_order': 'High',\n",
       " 'solver_type': 'Iterative'}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c1.sim.setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's change the name of the setup and increase the maximum number of passes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'LOM',\n",
       " 'reuse_selected_design': True,\n",
       " 'reuse_setup': True,\n",
       " 'freq_ghz': 5.0,\n",
       " 'save_fields': False,\n",
       " 'enabled': True,\n",
       " 'max_passes': 14,\n",
       " 'min_passes': 2,\n",
       " 'min_converged_passes': 2,\n",
       " 'percent_error': 0.5,\n",
       " 'percent_refinement': 30,\n",
       " 'auto_increase_solution_order': True,\n",
       " 'solution_order': 'High',\n",
       " 'solver_type': 'Iterative'}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c1.sim.setup.name = 'LOM'\n",
    "c1.sim.setup.max_passes = 14\n",
    "\n",
    "#To change multiple settings use the following method:\n",
    "#c1.sim.setup_update(name = 'LOM', max_passes = 14)\n",
    "\n",
    "c1.sim.setup\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our analysis we will pick the first qubit, Q_0_0, making sure to apply open terminations to the connection pads. The default bounding box is sufficient for a simple analysis. By passing the parameter `components` to the `sim.run()` method, the design is rendered automatically. Not passing it skips the rendering and tries to run the analysis on the latest design. If a design is not found, the full metal design is rendered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 09:14PM [connect_project]: Connecting to Ansys Desktop API...\n",
      "INFO 09:14PM [load_ansys_project]: \tOpened Ansys App\n",
      "INFO 09:14PM [load_ansys_project]: \tOpened Ansys Desktop v2020.2.0\n",
      "INFO 09:14PM [load_ansys_project]: \tOpened Ansys Project\n",
      "\tFolder:    C:/Users/LocalAdmin/Documents/Ansoft/\n",
      "\tProject:   Project3\n",
      "INFO 09:14PM [connect_design]: No active design found (or error getting active design).\n",
      "INFO 09:14PM [connect]: \t Connected to project \"Project3\". No design detected\n",
      "INFO 09:14PM [connect_design]: \tOpened active design\n",
      "\tDesign:    Design_q3d [Solution type: Q3D]\n",
      "WARNING 09:14PM [connect_setup]: \tNo design setup detected.\n",
      "WARNING 09:14PM [connect_setup]: \tCreating Q3D default setup.\n",
      "INFO 09:14PM [get_setup]: \tOpened setup `Setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
      "INFO 09:14PM [get_setup]: \tOpened setup `LOM`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
      "INFO 09:14PM [analyze]: Analyzing setup LOM\n",
      "INFO 09:15PM [get_matrix]: Exporting matrix data to (C:\\Users\\LOCALA~1\\AppData\\Local\\Temp\\tmp_hlqtijq.txt, C, , LOM:LastAdaptive, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 1, False\n",
      "INFO 09:15PM [get_matrix]: Exporting matrix data to (C:\\Users\\LOCALA~1\\AppData\\Local\\Temp\\tmpm6o1w2x7.txt, C, , LOM:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 1, False\n",
      "INFO 09:15PM [get_matrix]: Exporting matrix data to (C:\\Users\\LOCALA~1\\AppData\\Local\\Temp\\tmp8li0tjtq.txt, C, , LOM:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 2, False\n",
      "INFO 09:15PM [get_matrix]: Exporting matrix data to (C:\\Users\\LOCALA~1\\AppData\\Local\\Temp\\tmpupxqipgp.txt, C, , LOM:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 3, False\n",
      "INFO 09:15PM [get_matrix]: Exporting matrix data to (C:\\Users\\LOCALA~1\\AppData\\Local\\Temp\\tmpkjbblmmc.txt, C, , LOM:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 4, False\n",
      "INFO 09:15PM [get_matrix]: Exporting matrix data to (C:\\Users\\LOCALA~1\\AppData\\Local\\Temp\\tmp239ustm9.txt, C, , LOM:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 5, False\n",
      "INFO 09:15PM [get_matrix]: Exporting matrix data to (C:\\Users\\LOCALA~1\\AppData\\Local\\Temp\\tmp77k7odfy.txt, C, , LOM:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 6, False\n",
      "INFO 09:15PM [get_matrix]: Exporting matrix data to (C:\\Users\\LOCALA~1\\AppData\\Local\\Temp\\tmpbga72yox.txt, C, , LOM:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 7, False\n",
      "INFO 09:15PM [get_matrix]: Exporting matrix data to (C:\\Users\\LOCALA~1\\AppData\\Local\\Temp\\tmppkxuaoh7.txt, C, , LOM:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 8, False\n",
      "INFO 09:15PM [get_matrix]: Exporting matrix data to (C:\\Users\\LOCALA~1\\AppData\\Local\\Temp\\tmpan3h64py.txt, C, , LOM:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 9, False\n",
      "INFO 09:15PM [get_matrix]: Exporting matrix data to (C:\\Users\\LOCALA~1\\AppData\\Local\\Temp\\tmperxra0to.txt, C, , LOM:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 10, False\n"
     ]
    }
   ],
   "source": [
    "c1.sim.run(components=['Q_0_0'], open_terminations=[('Q_0_0', 'B0'), ('Q_0_0', 'B1'), ('Q_0_0', 'B2'), ('Q_0_0', 'B3')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then use the results of that simulation to complete a LOM analysis. The input values should be modified based on the simulation passes and the set frequencies of the resonators."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4, 5] [6 0 1 2]\n",
      "Predicted Values\n",
      "\n",
      "Transmon Properties\n",
      "f_Q 4.738954 [GHz]\n",
      "EC 233.980931 [MHz]\n",
      "EJ 13.273404 [GHz]\n",
      "alpha -265.921577 [MHz]\n",
      "dispersion 2.961037 [KHz]\n",
      "Lq 12.305036 [nH]\n",
      "Cq 82.785499 [fF]\n",
      "T1 38.984271 [us]\n",
      "\n",
      "**Coupling Properties**\n",
      "\n",
      "tCqbus1 -14.941228 [fF]\n",
      "gbus1_in_MHz -171.865495 [MHz]\n",
      "χ_bus1 -4.093768 [MHz]\n",
      "1/T1bus1 3932.655118 [Hz]\n",
      "T1bus1 40.470099 [us]\n",
      "\n",
      "tCqbus2 1.214466 [fF]\n",
      "gbus2_in_MHz 12.829544 [MHz]\n",
      "χ_bus2 -0.046240 [MHz]\n",
      "1/T1bus2 4.837406 [Hz]\n",
      "T1bus2 32900.886145 [us]\n",
      "\n",
      "tCqbus3 7.440809 [fF]\n",
      "gbus3_in_MHz 81.008269 [MHz]\n",
      "χ_bus3 -1.413122 [MHz]\n",
      "1/T1bus3 143.085528 [Hz]\n",
      "T1bus3 1112.306360 [us]\n",
      "\n",
      "tCqbus4 -0.961558 [fF]\n",
      "gbus4_in_MHz -10.815462 [MHz]\n",
      "χ_bus4 -0.019950 [MHz]\n",
      "1/T1bus4 1.964458 [Hz]\n",
      "T1bus4 81017.238470 [us]\n",
      "Bus-Bus Couplings\n",
      "gbus1_2 30.753259 [MHz]\n",
      "gbus1_3 54.127186 [MHz]\n",
      "gbus1_4 37.699733 [MHz]\n",
      "gbus2_3 13.610983 [MHz]\n",
      "gbus2_4 18.527072 [MHz]\n",
      "gbus3_4 16.145313 [MHz]\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fQ</th>\n",
       "      <th>EC</th>\n",
       "      <th>EJ</th>\n",
       "      <th>alpha</th>\n",
       "      <th>dispersion</th>\n",
       "      <th>gbus</th>\n",
       "      <th>chi_in_MHz</th>\n",
       "      <th>χr MHz</th>\n",
       "      <th>gr MHz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.044089</td>\n",
       "      <td>267.10336</td>\n",
       "      <td>13.273404</td>\n",
       "      <td>-306.945764</td>\n",
       "      <td>11.079517</td>\n",
       "      <td>[-161.15808571895315, 9.870283884905112, 71.93...</td>\n",
       "      <td>[-5.621652036035947, -0.05004703405751782, -1....</td>\n",
       "      <td>5.621652</td>\n",
       "      <td>161.158086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.95129</td>\n",
       "      <td>256.767469</td>\n",
       "      <td>13.273404</td>\n",
       "      <td>-294.059523</td>\n",
       "      <td>7.54795</td>\n",
       "      <td>[-146.4624487976284, 6.691015658791751, 73.940...</td>\n",
       "      <td>[-4.035714553638353, -0.01892349474338407, -1....</td>\n",
       "      <td>4.035715</td>\n",
       "      <td>146.462449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.872128</td>\n",
       "      <td>248.132548</td>\n",
       "      <td>13.273404</td>\n",
       "      <td>-283.352888</td>\n",
       "      <td>5.376871</td>\n",
       "      <td>[-155.3563666804045, 10.251305545805131, 74.91...</td>\n",
       "      <td>[-4.042454405786888, -0.037933542547650835, -1...</td>\n",
       "      <td>4.042454</td>\n",
       "      <td>155.356367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.846487</td>\n",
       "      <td>245.371362</td>\n",
       "      <td>13.273404</td>\n",
       "      <td>-279.940522</td>\n",
       "      <td>4.805843</td>\n",
       "      <td>[-160.4881170954147, 9.098286776764787, 76.290...</td>\n",
       "      <td>[-4.157072524571879, -0.02843345284637578, -1....</td>\n",
       "      <td>4.157073</td>\n",
       "      <td>160.488117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.804421</td>\n",
       "      <td>240.879201</td>\n",
       "      <td>13.273404</td>\n",
       "      <td>-274.400635</td>\n",
       "      <td>3.986857</td>\n",
       "      <td>[-163.84899918863601, 10.723100629455065, 77.9...</td>\n",
       "      <td>[-4.080058098970338, -0.03646057667869494, -1....</td>\n",
       "      <td>4.080058</td>\n",
       "      <td>163.848999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.782051</td>\n",
       "      <td>238.509452</td>\n",
       "      <td>13.273404</td>\n",
       "      <td>-271.484001</td>\n",
       "      <td>3.604898</td>\n",
       "      <td>[-166.45252998445875, 11.937440533798032, 78.9...</td>\n",
       "      <td>[-4.079413824028787, -0.04333548934732217, -1....</td>\n",
       "      <td>4.079414</td>\n",
       "      <td>166.452530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.762294</td>\n",
       "      <td>236.427395</td>\n",
       "      <td>13.273404</td>\n",
       "      <td>-268.92477</td>\n",
       "      <td>3.295459</td>\n",
       "      <td>[-168.75220986791896, 12.216219488988354, 79.9...</td>\n",
       "      <td>[-4.0778710744752615, -0.04375451018537349, -1...</td>\n",
       "      <td>4.077871</td>\n",
       "      <td>168.752210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.749929</td>\n",
       "      <td>235.129578</td>\n",
       "      <td>13.273404</td>\n",
       "      <td>-267.331082</td>\n",
       "      <td>3.114259</td>\n",
       "      <td>[-170.2243348027099, 12.30778022628425, 80.502...</td>\n",
       "      <td>[-4.078025496422903, -0.043416356394959575, -1...</td>\n",
       "      <td>4.078025</td>\n",
       "      <td>170.224335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.738954</td>\n",
       "      <td>233.980931</td>\n",
       "      <td>13.273404</td>\n",
       "      <td>-265.921577</td>\n",
       "      <td>2.961037</td>\n",
       "      <td>[-171.86549487771424, 12.829544193035785, 81.0...</td>\n",
       "      <td>[-4.093768494072426, -0.04623997642648877, -1....</td>\n",
       "      <td>4.093768</td>\n",
       "      <td>171.865495</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         fQ          EC         EJ       alpha dispersion  \\\n",
       "1  5.044089   267.10336  13.273404 -306.945764  11.079517   \n",
       "2   4.95129  256.767469  13.273404 -294.059523    7.54795   \n",
       "3  4.872128  248.132548  13.273404 -283.352888   5.376871   \n",
       "4  4.846487  245.371362  13.273404 -279.940522   4.805843   \n",
       "5  4.804421  240.879201  13.273404 -274.400635   3.986857   \n",
       "6  4.782051  238.509452  13.273404 -271.484001   3.604898   \n",
       "7  4.762294  236.427395  13.273404  -268.92477   3.295459   \n",
       "8  4.749929  235.129578  13.273404 -267.331082   3.114259   \n",
       "9  4.738954  233.980931  13.273404 -265.921577   2.961037   \n",
       "\n",
       "                                                gbus  \\\n",
       "1  [-161.15808571895315, 9.870283884905112, 71.93...   \n",
       "2  [-146.4624487976284, 6.691015658791751, 73.940...   \n",
       "3  [-155.3563666804045, 10.251305545805131, 74.91...   \n",
       "4  [-160.4881170954147, 9.098286776764787, 76.290...   \n",
       "5  [-163.84899918863601, 10.723100629455065, 77.9...   \n",
       "6  [-166.45252998445875, 11.937440533798032, 78.9...   \n",
       "7  [-168.75220986791896, 12.216219488988354, 79.9...   \n",
       "8  [-170.2243348027099, 12.30778022628425, 80.502...   \n",
       "9  [-171.86549487771424, 12.829544193035785, 81.0...   \n",
       "\n",
       "                                          chi_in_MHz    χr MHz      gr MHz  \n",
       "1  [-5.621652036035947, -0.05004703405751782, -1....  5.621652  161.158086  \n",
       "2  [-4.035714553638353, -0.01892349474338407, -1....  4.035715  146.462449  \n",
       "3  [-4.042454405786888, -0.037933542547650835, -1...  4.042454  155.356367  \n",
       "4  [-4.157072524571879, -0.02843345284637578, -1....  4.157073  160.488117  \n",
       "5  [-4.080058098970338, -0.03646057667869494, -1....  4.080058  163.848999  \n",
       "6  [-4.079413824028787, -0.04333548934732217, -1....  4.079414  166.452530  \n",
       "7  [-4.0778710744752615, -0.04375451018537349, -1...  4.077871  168.752210  \n",
       "8  [-4.078025496422903, -0.043416356394959575, -1...  4.078025  170.224335  \n",
       "9  [-4.093768494072426, -0.04623997642648877, -1....  4.093768  171.865495  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c1.setup.junctions = Dict({'Lj': 12.31, 'Cj': 2})\n",
    "c1.setup.freq_readout = 6.6\n",
    "c1.setup.freq_bus = [6.0, 6.2,6.4]\n",
    "\n",
    "c1.run_lom()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you are done with your analysis, please close it with `close()`. This will free up resources currently occupied by qiskit-metal to communiate with the tool."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "c1.sim.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GDS Render"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "chip_gds = design.renderers.gds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "chip_gds.options['no_cheese']['buffer'] = '50um'\n",
    "chip_gds.options['path_filename'] = '../../resources/Fake_Junctions.GDS'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chip_gds.export_to_gds(\"NxN_Chip.gds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can close the GUI now that we have finished the design."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gui.main_window.close()"
   ]
  }
 ],
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   "codemirror_mode": {
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